The detection of the concentration level of glucose or other analytes in certain individuals may be vitally important to their health. For example, the monitoring of glucose levels is particularly important to individuals with diabetes or pre-diabetes. People with diabetes may need to monitor their glucose levels to determine when medication (e.g., insulin) is needed to reduce their glucose levels or when additional glucose is needed.
Devices have been developed for automated in vivo monitoring of analyte time series characteristics, such as glucose levels, in bodily fluids such as in the blood stream or in interstitial fluid. Some of these analyte level measuring devices are configured so that at least a portion of a sensor of an on-body device is positioned below a skin surface of a user, e.g., in a blood vessel or in the subcutaneous tissue of a user. As used herein, the term analyte monitoring system is used to refer to any type of in vivo monitoring system that uses a sensor disposed with at least a subcutaneous portion to measure and store sensor data representative of analyte concentration levels automatically over time. Analyte monitoring systems include both (1) systems such as continuous glucose monitors (CGMs) which transmit sensor data continuously or at regular time intervals (e.g., once per minute) to a processor/display unit and (2) systems that transfer stored sensor data in one or more batches in response to a scan request from a processor/display unit (e.g., based on an activation action and/or proximity using, for example, a near field communications protocol).
Monitoring a user's analyte level that has a relatively high rate of change can require collecting and storing a relatively large amount of sensor data. However, in an effort to minimize the cost, size, and power requirements of analyte monitoring systems, it is desirable to minimize the amount of memory required for use within the on-body device. Thus, what is needed are systems, methods and apparatus that can provide a sufficient amount of sensor data to accurately determine a user's analyte level but at the same time minimize the memory requirements.